Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
An introduction to fuzzy control
An introduction to fuzzy control
IEEE Spectrum
Control of electrical drives based on fuzzy logic
WSEAS Transactions on Systems and Control
Stabilization of fuzzy control systems
WSEAS Transactions on Systems and Control
An approach to fuzzy control of nonlinear systems: stability and design issues
IEEE Transactions on Fuzzy Systems
WSEAS TRANSACTIONS on SYSTEMS
Pseudo-equivalence of fuzzy PID controllers
WSEAS Transactions on Systems and Control
Identification of non-linear systems, based on neural networks, with applications at fuzzy systems
ICAI'09 Proceedings of the 10th WSEAS international conference on Automation & information
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The paper presents a short review of some main properties of fuzzy systems, useful in control and other applications. Fuzzy rule bases and fuzzy systems may be seen as applications between fuzzy or real sets, with algebraic properties as: commutative law, neutral element and symmetric elements. Fuzzy systems, developed with different rule bases, fuzzy values, membership functions, fuzzyfication and defuzzification methods may be characterised with SISO and MISO transfer characteristics. A spatial sector property, useful in the stability analysis with Lyapunov techniques, may be emphasised, on these characteristics. Also, the fuzzy systems have a variable gain with its inputs. The transfer characteristics and system linearization are usefull in the design of fuzzy PID controllers. Based on the transient characteristics we are showing that the control systems based on fuzzy PI controllers assure better quality criteria then the control systems based on linear PI controllers and they are more robust.